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Enteromorpha compressa Macroalgal Biomass Nanoparticles as Eco-Friendly Biosorbents for the Efficient Removal of Harmful Metals from Aqueous Solutions
- Source :
- Analytica, Vol 5, Iss 3, Pp 322-342 (2024)
- Publication Year :
- 2024
- Publisher :
- MDPI AG, 2024.
-
Abstract
- This study focuses on the biosorption of harmful metals from aqueous solutions using Enteromorpha compressa macroalgal biomass nanoparticles as the biosorbent. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction analysis (XRD) were employed to characterize the biosorbent. The effects of pH, initial metal ion concentration, biosorbent dosage, and contact time on the biosorption process were investigated. The maximum biosorption capacity for metals was observed at a pH of 5.0. The experimental equilibrium data were analyzed using three-parameter isotherm models, namely Freundlich, Temkin, and Langmuir equations, which provided better fits for the equilibrium data. A contact time of approximately 120 min was required to achieve biosorption equilibrium for various initial metal concentrations. Cr(III), Co(II), Ni(II), Cu(II), and Cd(II) demonstrated distinct maximum biosorption capacities of 24.99375 mg/g, 25.06894 mg/g, 24.55796 mg/g, 24.97502 mg/g, and 25.3936 mg/g, respectively. Different kinetic models were applied to fit the kinetic data, including intraparticle diffusion, pseudo-second-order, and pseudo-first-order versions. The pseudo-second-order model showed good agreement with the experimental results, indicating its suitability for describing the kinetics of the biosorption process. Based on these findings, it can be stated that E. compressa nanoparticle demonstrates potential as an effective biosorbent for removing targeted metals from water.
Details
- Language :
- English
- ISSN :
- 26734532 and 98242814
- Volume :
- 5
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- Analytica
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.285fc98242814e958379f73bea8b6592
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/analytica5030021